| Cite this article: | 王梅凯,赵成萍,李博,等.基于ATI-BP的土壤墒情反演研究[J].灌溉排水学报,0,():-. |
| wangmeikai,zhaochengping,libo,et al.基于ATI-BP的土壤墒情反演研究[J].灌溉排水学报,0,():-. |
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| DOI： |
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| Soil Moisture Inversion Based on ATI-BP |
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wangmeikai1, zhaochengping1, libo2, zhouxinzhi1
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1.Institute of intelligent control, sichuan university;2.Joint laboratory of water conservancy informatization, sichuan university
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| Abstract: |
| Soil moisture monitoring plays an important role in crop growth and rational distribution of water resources.【Objective】 to improve the accuracy and universality of remote sensing moisture inversion,【Method】in this paper, MODIS(MOD021KM) data was used to establish a moisture inversion model based on modified apparent thermal inertia. On this basis, BP artificial neural network (BPNN) was combined for collaborative inversion.Firstly, considering the limitation of soil moisture inversion with a single apparent thermal inertia, EVI and ATI were selected as the evaluation indexes of soil moisture inversion, and the measured soil moisture was used as the validation indexes. After the pretreatment of the original remote sensing images, the evaluation indexes were calculated, and 25 groups of soil moisture at 10cm depth were measured on the spot.Then, the BPNN soil moisture inversion model was constructed with the evaluation index as the input layer and the validation index as the output layer.【Results】 this paper USES the method of mean square error as the measure, selected as the research object in dujiangyan irrigation area has good inversion results, the model of evaluation index and 10 cm depth of soil moisture has good correlation, the comprehensive factors of vegetation coverage and apparent thermal inertia inversion of soil moisture in the irrigation area, the final inversion mean square error is 0.0039, compared with the linear and logarithmic and power function, and the relative errors were reduced by 66.9%, 81.1% and 74.9%.【Conclusion】the accuracy of soil moisture inversion with this model has been significantly improved.Depending on remote sensing image and partial measured data, this method has a good reference value in the study of inversion of large area moisture content. |
| Key words: MODIS；BP Neural Network；ATI；EVI；Dujiangyan Irrigation Area；Soil Moisture Inversion |
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